Yukim1andHongsheng Liu
793d3e9ab7
Signed-off-by: zwhzzz0821 <2831474076@qq.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
74 lines
2.7 KiB
Python
74 lines
2.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Ming TTS pipeline: Stage-0 LLM+flow -> Stage-1 audio VAE."""
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from vllm_omni.config.stage_config import (
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PipelineConfig,
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StageExecutionType,
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StagePipelineConfig,
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)
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_PROC = "vllm_omni.model_executor.stage_input_processors.ming_tts"
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MING_TTS_PIPELINE = PipelineConfig(
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model_type="ming_tts",
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default_deploy_config_name="ming_tts.yaml",
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model_arch="MingTTSForConditionalGeneration",
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hf_architectures=("MingTTSForConditionalGeneration", "BailingMMNativeForConditionalGeneration"),
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# The dense (0.5B) and MoE (16.8B) share architectures=["BailingMMNativeForConditionalGeneration"]
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# Here we disambiguate by the model_type the upstream HF config reports
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hf_config_predicate=lambda c: str(getattr(c, "model_type", "")) == "dense",
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stages=(
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StagePipelineConfig(
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stage_id=0,
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model_stage="llm",
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execution_type=StageExecutionType.LLM_AR,
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input_sources=(),
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owns_tokenizer=True,
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hf_config_name="llm_config",
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engine_output_type="latent",
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async_chunk_process_next_stage_input_func=(f"{_PROC}.llm2audio_vae_async_chunk"),
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sampling_constraints={
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"temperature": 0.0,
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"top_p": 1.0,
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"top_k": -1,
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"max_tokens": 512,
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"detokenize": True,
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},
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),
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StagePipelineConfig(
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stage_id=1,
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model_stage="audio_vae",
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execution_type=StageExecutionType.LLM_GENERATION,
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input_sources=(0,),
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final_output=True,
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final_output_type="audio",
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hf_config_name="llm_config",
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engine_output_type="audio",
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sync_process_input_func=f"{_PROC}.llm2audio_vae",
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sampling_constraints={
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"temperature": 0.0,
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"top_p": 1.0,
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"top_k": -1,
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"max_tokens": 1,
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"detokenize": False,
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},
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),
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),
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)
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# MoE variant (inclusionAI/Ming-omni-tts-16.8B-A3B)
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# Same two-stage topology and model class as the above,
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# but with a distinct model_type reported by the upstream HF config.
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# Keep this for auto-detection of deploy config yaml.
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MING_TTS_MOE_PIPELINE = PipelineConfig(
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model_type="ming_tts_moe",
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default_deploy_config_name="ming_tts_moe.yaml",
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model_arch="MingTTSForConditionalGeneration",
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hf_architectures=("MingTTSForConditionalGeneration", "BailingMMNativeForConditionalGeneration"),
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# Disambiguate from the dense-0.5B ming_tts pipeline
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hf_config_predicate=lambda c: str(getattr(c, "model_type", "")) == "bailingmm",
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stages=MING_TTS_PIPELINE.stages,
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)
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